Negative image for sign placement detection

ABSTRACT

Systems, methods, and apparatuses are described for a negative image or false positive profile for sign locations. Image data or another type of optical data is collected along a path by a collection device such as a camera. The data is analyzed to identify one or more false positive locations along the path at which signs for other paths may be detected. The false positive locations may be described in the negative image or false positive profile. Additional or subsequent optical data may be analyzed based on the negative image or false positive profile may be analyzed to identify at least one confirmed sign position.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation under 35 U.S.C § 120 and 37 C.F.R. §1.53(b) of U.S. patent application Ser. No. 14/524,606 (now U.S. Pat.No. 9,530,313) filed Oct. 27, 2014, the disclosure of which isincorporated herein by reference in its entirety.

FIELD

The following disclosure relates to sign identification, or moreparticularly, identification and prevention of false positives in signidentification.

BACKGROUND

Nearly all roadways are posted with physical speed limit signs. Thespeed limits may be set according to a wide variety of rules and bydifferent entities. Countries or states may set speed limits forinterstates or major highways, and municipalities such as cities andtowns may set speed limits for smaller roads and streets. The speedlimits may be selected under rules based on the curvature or lane widthof the road. The speed limits may be selected under rules based on theproximity to urban or rural areas. Other considerations such as schoolzones, bridges, or pedestrian crossings may impact the selection ofspeed limits. Because of these variable situations, it may not bepossible to reliably identify the speed limit of a road from theplacement of the road or shape of the road on the map. Instead, postedspeed limits are detected in order to identify speed limits of roads onthe map. Other indicia or road postings may be similarly inconsistent.

Once speed limits or other indicia or road postings are detected, thedata may be associated with the appropriate road link. Because ofvariances in sign locations, this may not be a straightforward process.Challenges remain in reliably associating correct sign values with theirrespective road links.

SUMMARY

In one embodiment, optical data collected along a path by a collectiondevice is received at a computing device. From the optical data, one ormore false positive locations along the path are identified. The opticaldata is further analyzed as a function of the one or more false positivelocations to locate at least one confirmed sign position in the opticaldata. In some embodiments, a false positive profile is generated basedon the false positive locations along the path. The false positiveprofile may be associated with a link corresponding to the path on whichthe optical data was collected.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments are described herein with reference to thefollowing drawings.

FIG. 1 illustrates an example system for determining sign placementfalse positives.

FIG. 2 illustrates an example set of roadways with potential falsepositives for sign placement.

FIG. 3 illustrates another example set of roadways with potential falsepositives for sign placement.

FIG. 4 illustrates another example set of roadways with potential falsepositives for sign placement.

FIG. 5 illustrates an overhead view of an example set of roadways withpotential false positives for sign placement.

FIG. 6 illustrates an example bounding box for the potential falsepositives of FIG. 5.

FIG. 7 illustrates example vehicles for determining potential falsepositives.

FIG. 8 illustrates an example map of sign locations.

FIG. 9 illustrates an example scheme for determining potential falsepositives for sign placement in the map of FIG. 8.

FIG. 10 illustrates an example mobile device for the system of FIG. 1.

FIG. 11 illustrates an example flowchart for determining potential falsepositives for sign placement.

FIG. 12 illustrates an example network device of the system of FIG. 1.

FIG. 13 illustrates an example flowchart for determining potential falsepositives for sign placement.

DETAILED DESCRIPTION

A road sign may be defined as a physical object conveying information tothose traveling on or near a road. Signs may be posted on existing postsor utility poles, individually mounted on poles adjacent to the road,mounted on walls or other building alongside the road, mounted onoverpasses, or supported by overhead gantries. Signs intended for oneroad may be posted within a line of sight for another road. The camerasor other sensors on a vehicle traveling on one road may be able todetect signs intended for another road.

The proximity of adjacent roads may cause false positives in signdetection. A false positive for sign detection is when a sign isdetected for a location but no sign exists at that location, or the signthat does exist belongs to another road or path. For example, consider ahighway with a parallel access road that runs alongside the highway.Signs for the access road may be detected by a vehicle traveling on thehighway, and signs for the highway may be detected by a vehicletraveling on the access road. In another example, signs for a roadwaymay be detected from vehicles traveling on an overpass, or signs for theoverpass may be detected from vehicles traveling on the lower roadway.Signs from a ramp may be detected from a main roadway, and vice versa.Similar scenarios are possible in many types of intersections.

When a sign is detected from a nearby roadway, data from the sign may beassociated with the wrong roadway. Such incorrect detections orassociations may be referred to as false positives. When false positivesare stored, users may be given incorrect guidance or other information.For example, when a speed limit sign is associated with the wrongroadway, the roadway may be assigned a speed limit that is too low.Accordingly, a navigation device may issue incorrect speed warnings whenthe vehicle is actually not exceeding the real speed limit. In addition,the navigation device may redirect a user based on the incorrect speedlimit. The incorrect speed limit may cause the route to appear slowerthan it really is, and an alternative road may be selected when theoriginal roadway would have been the best route.

The following embodiments provide systems and methods for theidentification of false positives in sign detection and algorithms todetermine when the correct sign is detected. Data is collected over aselection of one or more roadways and by one or multiple vehiclesequipped with cameras or other sensors to detect the signs. A negativeimage or false positive profile for a road link may be generated. Thenegative image indicates locations of known or potential false positivelocations. For subsequent detection of road signs, the negative imagemay instruct the camera or other sensor to ignore particular areas orremove particular detected sign locations from the detection results.

Overview

FIG. 1 illustrates an example system 120 for identification of falsepositives in sign detection data collected by one or more vehicles 124.The system 120 includes a developer system 121, one or more mobiledevices 122, a workstation 128, and a network 127. Additional,different, or fewer components may be provided. For example, many mobiledevices 122 and/or workstations 128 connect with the network 127. Thedeveloper system 121 includes a server 125 and one or more databases.The database 123 may include probe data or data collected from one ormore vehicles 124, which may include both historical and real time data.The database 123 may be a geographic database including road links orsegments. In one embodiment, the system 120 may determine positions forfalse positives. In another embodiment, the system 120 may apply thefalse positives to subsequent detected sign locations.

The database 123 may store or maintain geographic data such as, forexample, road segment or link data records and node data records. Thelink data records are links or segments representing the roads, streets,or paths. The node data records are end points (e.g., intersections)corresponding to the respective links or segments of the road segmentdata records. The road link data records and the node data records mayrepresent, for example, road networks used by vehicles, cars, and/orother entities.

The mobile device 122 may receive optical data collected along one ormore paths by a collection device such as sensor 126. The sensor 126 maybe a camera or another optical sensor such as a light detection andranging (LIDAR) device. The mobile device 122 may send the optical datato the server 125, and the server 125 receives the optical data throughnetwork 127.

Either the mobile device 122 or the server 125 may process the opticaldata. For example, either device may identify one or more false positivelocations along the path. The false positive location for a road signmay be a location that is detected for the road sign but does notactually include a road sign. For example, when another object such as atree or building is incorrectly identified as a road sign. The falsepositive location for a road sign may be a label or attribute for a roadsign that is associated with a road segment or link but should not beassociated with that road segment or link. For example, when a road signfor a nearby road can be detected from one or more other roads.

The false positive locations may be identified through visualinspection. That is, a user may look at the optical data and determinewhether a particular sign should be associated with the path of thevehicle 124. Other techniques, such as computer vision or learned modelapproaches may be used. In one example, a user of the mobile device 122is prompted upon the collection of the optical data to identify whethera sign depicted in the optical data should be recorded as a signlocation or dismissed as a false positive.

The location of the identified false positive location may be stored inthe database 123. The location may be stored as a set of coordinates.The location may be stored in association with the link upon which theoptical data was collected. In one example, the mobile device 122generates location data from position circuitry (e.g., globalpositioning system (GPS)). The current road link may be selected fromthe database 123. The identified false positive may be stored for thecurrent road link in the database 123.

Another vehicle 124 including another mobile device 122 may subsequentlycollect optical data. The mobile device 122 may send location dataindicating the same road link that is associated with a false positivesign location. The mobile device 122 or the server 125 may analyzesubsequently collected optical data in light of the false positive signlocation. For example, the mobile device 122 or server 125 may ignoreportions of the optical data that correspond to the false positive signlocation. The mobile device 122 or server 125 may identify an actualsign position in other portions of the optical data. Additionalembodiments and details are discussed below.

The mobile device 122 may be a personal navigation device (“PND”), aportable navigation device smart phone, a mobile phone, a personaldigital assistant (“PDA”), a tablet computer, a notebook computer,and/or any other known or later developed mobile device or personalcomputer. Non-limiting embodiments of navigation devices may alsoinclude relational database service devices, mobile phone devices, orcar navigation devices.

The developer system 121, the workstation 128, and the mobile device 122are coupled with the network 127. The phrase “coupled with” is definedto mean directly connected to or indirectly connected through one ormore intermediate components. Such intermediate components may includehardware and/or software-based components.

The computing resources may be divided between the server 125 and themobile device 122. In some embodiments, the server 125 performs amajority of the processing for calculating the vehicle confidence valueand the comparison with the confidence threshold. In other embodiments,the mobile device 122 or the workstation 128 performs a majority of theprocessing. Alternatively, the processing is divided substantiallyevenly between the server 125 and the mobile device 122 or workstation128.

Example Path and Sign Arrangements

FIGS. 2, 3, and 4 illustrate example sets of roadways with potentialfalse positives for sign placement. The images of FIGS. 2, 3, and 4 maycorrespond to the optical data describing one or more road signs ascollected terrestrially by a camera on vehicle 124. The term terrestrialimage may refer to those images taken from the ground or near the ground(i.e., not aerial images or satellite images). Alternatively, anyembodiment described herein may be applied to aerial images or satelliteimages. Rather than roadways, other types of paths may be used such aspedestrian paths, bike paths, waterways, airways, train paths, movingwalkways or other paths.

FIG. 2 illustrates an example where multiple roads traveling in the samedirection are adjacent and parallel. The term adjacent may be defined aswithin a distance range. The distance range may be within the detectioncapabilities of sensor 126. The distance range may be a function of thefocal length of the camera. The distance range may be a function of therange of a LIDAR device. The distance range may be configurable. Exampledistance ranges may be 10 meters, 100 feet or another value.

FIG. 2 illustrates that a vehicle traveling on left road 152 may bewithin viewing range or detection range of sign 135 intended for leftroad 152 and also sign 136 intended for right road 153. Likewise, avehicle traveling on right road 153 may be within viewing range ordetection range of sign 136 intended for right road 153 and also sign135 intended for left road 152. When building a false positive profilefor left road 152, the server 125 may identify a false positive locationat sign 136, and when building a false positive profile for right road153, the server 125 may identify a false positive location at sign 135.Accordingly, when implementing the false positive profile, the locationof sign 136 may be ignored for left road 152 and the location of sign135 may be ignored for right road 153.

FIG. 3 illustrates that a vehicle traveling on left lane 154 a may bewithin viewing range or detection range of sign 137 intended for theleft lane 154 b but also within range of sign 138 intended for rightlane 154 a and sign 139 intended for exit ramp 155. Similarly, a vehicletraveling on right lane 154 b may mistakenly detect signs 137 and 139intended for other vehicles, and a vehicle traveling on exit ramp 155may mistakenly detect signs 137 and 138 intended for other vehicles. Theserver 125 may identify false positive locations for left lane 154 a atthe locations of signs 138 and 139. The server 125 may identify falsepositive locations for right lane 154 b at the locations of signs 137and 139. The server 125 may identify false positive locations for exitramp 155 at the locations of signs 137 and 138. The false positivelocations may be ignored when subsequently analyzing optical data forsign locations.

FIG. 4 illustrates another example where multiple signs may bedetectable from a single point. Sign 141 should be associated with roadlink 156, sign 143 should be associated with road link 157, and sign 142should be associated with road link 158. However, vehicles on road link156 may be able to detect signs 142 and 143. Accordingly, the mobiledevice 122 or server 125 may identify false positive locations for eachroad link, and analyze subsequent optical data taking into considerationthe identified false positive locations.

The units for the speed limit signs may be in kilometers per hour, milesper hour, meters per second, or other units. Some of the signs areillustrated as speed limit signs but other types of signs are possible.The other signs may be any type of sign that instructs a driver andcould result in a change in the operation of the vehicle. The othertypes of signs may include curve warnings, road identification signs,navigational signs (e.g., distance to location A or turn here forlocation A), passing zones, yield signs, stop signs, bus stops,crosswalk signs, taxi stand, school zones, jail zones, high occupancyvehicle signs, express lane signs (e.g., directional or operationstatus), exit signs, parking signs, exclusionary signs, traffic signs,closure signs, toll signs, street or road identification signs, oranother type of sign with alphanumeric and/or graphical indicia.

False Positive Profile or Negative Image Generation

The negative image may be a false positive profile or a set of data thatdescribes the locations of one or more false positives. The falsepositive profile may list road links paired with false positivelocations. The false positive profile may be stored in a lookup table ina memory (e.g., database 123, a memory of mobile device 122, or a memoryof server 125). An example association of road link identifiers andfalse positive locations is shown in Table 1. In some circumstances, asingle false positive location may be associated with multiple roadlinks.

TABLE 1 Road Link False Positive(s) A4352 (X₁, Y₁) B2635 (X₂, Y₂), (X₃,Y₃) B3487 (X₃, Y₃), (X₄, Y₄)

FIG. 5 illustrates an overhead view of an example set of roadways withpotential false positives for sign placement. A first roadway (roadlink) 256 a runs left to right, and a second roadway (road link) 256 bruns up and down. A first vehicle 151 travelling on the road link 256 amay detect signs 253, 254, and 255. A second vehicle 150 traveling onthe road link 256 b may detect signs 252, 253, 254, and 255.

Either or both of the vehicles may traverse the roadways and collectoptical data or images for building the false positive profile. Thefalse positive profile may be built using a variety of techniques. Falsepositives for any link may be identified in collected optical data basedon one or a combination of (1) visual inspection, (2) crowdsourcing, (3)computer aided analysis, (4) statistical analysis, or (5) inference fromadjacent links.

The optical data may be visually inspected. A user (e.g., technician ormap developer) may inspect image data (e.g., FIGS. 2-4) and determinewhich of the signs should be associated with the road link that theimage was collected from. The general placement of the road link thatthe image was collected from may be inferred from the perspective andangle of the image. The user may click a check box or touch signs thatshould be associated with another road link.

The visual inspection may be performed using a crowdsourcingapplication. For example, the image data including sign data may be sentusing network 127 (e.g., the Internet) to a plurality of other users.The users may inspect the image and determine which of the signs shouldbe associated with the road link that the image was collected from. Inone example, the same image is sent to multiple other users to increasethe confidence level of the result. The users may vote and the condition(e.g., whether a particular sign is a false positive) receiving the mostvotes is selected. Alternatively, different users may be sent differentimages. The users may receive an incentive (e.g., a coupon or monetarycompensation) to inspect the image data for false positive signlocations.

The false positives may be identified with computer aided analysis. Forexample, the optical data may be analyzed using a computer visionapplication. The computer vision application may include one or acombination of edge detection, feature matching, object recognition, ormotion analysis. The computer vision application may identify potentialsigns in the optical data based on the shape and size of the signs. Theserver 125 may compare a set of template images to the optical data. Theset of template images may include common sizes and shapes for roadsigns.

The computer vision application may determine a distance from the roadlink and a placement angle with respect to the road link where the signoccurs in the optical data. The computer vision application maydetermine an orientation angle of the sign. The identification of falsepositives may be based on the distance, placement angle, or orientationof the signs. For example, signs at greater than a threshold distancefrom the road link may be designated as false positives for the roadlink. In another example, signs of a certain size that have a placementangle greater than a threshold may be designated as false positives forthe road link. Finally, signs at an orientation within a predeterminedrange may be designated as false positives for the road link. Associatedsigns should be at an orientation nearly perpendicular to the road link.False positive signs may have an orientation at an angle less than aminimum or more than a maximum angle from the road link. An examplerange from the minimum angle to the maximum angle may be 40 to 140degrees. Other orientation ranges are possible.

One alternative to the computer vision application for determining theplacement and distance of the signs may be LIDAR or another distancingtechnology (e.g., RADAR, structured light). In one example, the signsare painted with a reflective paint or retroreflective paint. The extrareflectivity may distinguish the signs from other objects in the opticaldata. The area of reflectivity may be easily measured to determine theplacement and distance of the signs. The resolution or accuracy of thedistance of the signs using this technique may be about 1 centimeter.

The false positives may be identified using a statistical analysis. Forexample, in the case of speed limit signs, the road link may beassociated with speed limit values. When a new sign for a speed limit iscollected for the road link, the new speed limit may be compared toprevious speed limit values. For example, when a difference between thenew speed limit and the historical speed limit values is more than apredetermined threshold, the new speed limit is designated as a falsepositive. Example predetermined thresholds include 30 miles per hour, 50kilometers per hour, or 50% of the value of the historical speed limitvalues. When multiple data points exist for speed limits for a roadlink, false positives may be designated when new data points exceed apredetermined number of standard deviations from the average values.

False positives may also be associated with a confidence level, whichmay also be stored in the lookup table and/or database 123. Theconfidence level may be a probability, percentage, or decimal value thatdescribes how likely the false positive is in fact a location of a signthat should not be associated with the current road link. The confidencevalue may be a function of the distance or placement of the detectedsign. Multiple threshold values may be used. For example, when the signis a first distance from the road link, the sign is designated as afalse positive with low confidence, and when the sign is a seconddistance (greater than the first distance) from the road link, the signis designated as a false positive with high confidence. Multipledistances and confidence levels may be used. Similarly, the orientationof the sign may be compared to multiple thresholds corresponding toconfidence levels.

Implementation of the False Positive Profile

FIG. 6 illustrates example bounding boxes for the potential falsepositives of FIG. 5. A bounding box is a blackout region or ignoredregion in the optical data. The bounding box may be defined in a placeor a three-dimensional space. The bounding box may be in a planeperpendicular to the direction of the path, in a plane perpendicular tothe line of sight of the camera or other sensor, or in a plane parallelto the ground. The bounding box may be rectangular, a rectangular prism,a circle, a sphere, or another shape. The size of the bounding box maybe configurable. The size of the bounding box may be set based on thesize of the sign, based on the capabilities of the sensor 126, the speedthat the optical data was collected, or based on the functionalclassification of the road link.

FIG. 6 illustrates an example bounding boxes for the vehicle 151traveling on road link 256 a. Bounding box 162 is located around thefalse positive sign location 252 (shown in FIG. 5), and bounding box 165is located around the false positive sign location 255 (shown in FIG.5). The server 125 or the mobile device 122 may be configured totranslate the optical data taken from multiple positions and thebounding box to the same coordinate system. A single bounding box may beapplied to multiple images.

When subsequent optical data is received at the server 125 or the mobiledevice 122, the false positive profile may be implemented by (1)blocking analysis within the bounding box, (2) subjecting dataassociated with the bounding box to higher scrutiny, or (3) issuing awarning to a developer or user indicating the risk of a false positive.

Analysis on the optical data may be blocked based on the bounding box.In one example, data within the bounding box is purged or removed fromthe set of optical or image data. Signs may be identified outside of thebounding box based on any of the algorithms described above (e.g.,computer vision). In another example, after a sign is detected in theoptical data, the location of the sign is compared to the bounding boxesto determine if the detected sign is within the bounding box and islikely a false positive.

The bounding box boundaries are illustrations of thresholds. Thebounding boxes may be stored numerically as coordinates of vertices ofthe bounding box. When a distance between the potential sign positionand a closest of the one or more false positive locations exceeds athreshold corresponding to the size of the bounding box, the potentialsign position may be designated as a confirmed sign position. When thedistance between the potential sign position and the closest of the oneor more false positive locations is less than the thresholdcorresponding to the size of the bounding box, the potential signposition may be deleted or removed from consideration.

The optical data or image data corresponding to the bounding box may besubjected to higher scrutiny. For example, any signs detected using acomputer vision application within a bounding box may be submitted forfurther visual inspection (manual analysis) by a human user or morecomputationally intensive computer inspection. In another example, asecond computer analysis is performed on detected signs in the boundingbox.

The server 125 or mobile device 122 may generate and issue a warning toa developer or other user indicating the risk of a false positive. Whensigns are detected in the bounding box, a message is generated informingthe user that there's a risk of a false positive. The message may alsoinclude the confidence level.

Assisted Driving Platforms

FIG. 7 illustrates example vehicles 124 for determining potential falsepositives. The vehicles 124 may be assisted driving vehicles. Assisteddriving vehicles include autonomous vehicles, highly assisted driving(HAD), and advanced driving assistance systems (ADAS). Any of theseassisted driving systems may be incorporated into mobile device 122.Alternatively, an assisted driving device 136 may be included in thevehicle 124. The assisted driving device may include memory, aprocessor, and systems to communicate with the mobile device 122.

The term autonomous vehicle may refer to a self-driving or driverlessmode in which no passengers are required to be on board to operate thevehicle. An autonomous vehicle may be referred to as a robot vehicle oran automated vehicle. The autonomous vehicle may include passengers, butno driver is necessary. These autonomous vehicles may park themselves ormove cargo between locations without a human operator. Autonomousvehicles may include multiple modes and transition between the modes.Autonomous vehicles may set a speed for the vehicle based on speed limitvalues from the database 123 or speed limit values detected by sensor126. Other commands may be generated based on other types of signs.

A highly assisted driving (HAD) vehicle may refer to a vehicle that doesnot completely replace the human operator. Instead, in a highly assisteddriving mode, the vehicle may perform some driving functions and thehuman operator may perform some driving functions. Vehicles may also bedriven in a manual mode in which the human operator exercises a degreeof control over the movement of the vehicle. The vehicles may alsoinclude a completely driverless mode. Other levels of automation arepossible. Autonomous vehicles may adjust a speed for the vehicle basedon speed limit values from the database 123 or speed limit valuesdetected by sensor 126. Other commands may be generated based on othertypes of signs.

Similarly, ADAS vehicles include one or more partially automated systemsin which the vehicle alerts the driver. The features are design to avoidcollisions automatically. Features may include adaptive cruise control,automate braking, traffic warnings, alerts for drivers of other cars,danger alerts, warnings to keep the driver in the correct lane, or blindspot warnings. ADAS vehicles may issue a speed warning for the vehiclebased on speed limit values from the database 123 or speed limit valuesdetected by sensor 126 and the current speed of the vehicle. Othercommands may be generated based on other types of signs.

In one example, the mobile device 122 or the assisted driving devicereceives speed data (e.g., based on GPS) for the vehicle 124. The speeddata is compared to the speed value read from one or more signs that arenot included in the false positive profile. When the current speed dataexceeds the detected speed value, a speed warning is issued. The speedwarning may indicate to the user that the speed limit is exceeded.Alternative, a speed reduction command may be generated thatautomatically causes the vehicle 124 to brake or decrease the throttle.

Autonomous vehicles, HAD vehicles, or ADAS vehicle may be used tocollect the optical data for building the false positive profiles. Inaddition, any of these types of vehicle may be controlled based on thefalse positive profiles and subsequently collected data. For example,the vehicles may collect images of road signs and analyze the road signson the fly. The analysis may be based on any of the embodiments herein.The vehicles may automatically change the speed of the vehicle, maketurns, select a new route, or other driving features based on theanalysis.

The vehicles 124 may be equipped with a mobile device 122 and a sensorarray including one or a combination of a vehicle sensor 113, anenvironment sensor 111, and a camera 115. One example camera 115 a ismounted on the top of the vehicle and has a 360 degree field of view,and another type of camera 115 b is mounted on a front, rear, or side ofthe vehicle 124 and has a wide angle view less than a 360 field of view.The mobile device 122 may be a personal device such as a mobile phoneequipped with position circuitry (e.g., global positioning system (GPS))and an inertial measurement unit (IMU). The mobile device 122 may be aspecialized device (e.g., not a mobile phone) mounted or otherwiseassociated with the vehicle 124 and similarly equipped with positioncircuitry and an IMU. Additional, different, or fewer components may beincluded.

The vehicle sensors 113 generate data based on detecting the operationof the vehicle 124. The vehicle sensors 113 may include a throttlesensor that measures a position of a throttle of the engine or aposition of an accelerator pedal, a brake senor that measures a positionof a braking mechanism or a brake pedal, or a speed sensor that measuresa speed of the engine or a speed of the vehicle wheels. In addition, thevehicle sensor 113 may include a steering wheel angle sensor, aspeedometer sensor, or a tachometer sensor. The mobile device 122 maygenerate vehicle commands based on data from the vehicle sensors 113 incombination with sign values described herein.

The environment sensors 111 generate data for identifying thesurroundings and location of the car. The environment sensors 111 mayinclude light detection and ranging (LIDAR), radar, pressure sensors,rain sensors, windshield wiper sensors, altimeter, barometers, lanesensors, proximity sensors, or other sensors. The mobile device 122 mayidentify likely weather (e.g. light rain, heavy rain, sun, snow, wind,or other weather features) or environmental conditions based on the dataoutput of the environment sensors 111. The mobile device 122 maygenerate vehicle commands based on data from the environment sensors 111in combination with sign values described herein.

Electronic Horizon

The false positive profile may be organized in a negative imageelectronic horizon. The electronic horizon describes upcoming falsepositive locations for a current route or current direction of travel.The electronic horizon refers to the collection of the road links andintersections leading out from a current vehicle position and falsepositives associated with those road links. The road links are potentialdriving paths of the vehicle from the current vehicle position. Theelectronic horizon may be limited to the current route or include allpossible links reachable from the current link for a predetermineddistance. The electronic horizon may include other data, including theroad attributes, road objects, and road geometry of the road segmentsthat form the electronic horizon. To calculate the electronic horizon,the location data indicating the vehicle's current position is used toquery the database 123.

In one example for building an electronic horizon for all possiblelinks, data for all of the road segments around the vehicle's currentposition is identified. A set of boundaries may be used to limit thesize of the electronic horizon. The boundaries may be set so that thepotential paths extending from the current vehicle position aresufficiently large so that the driver assistance programs are providedwith all the data they may need to perform their functions, given thespeed and direction of the vehicle as well as specific requirements ofeach of the driver assistance functions. On the other hand, boundariesfor an electronic horizon may be set as small as possible in order toreduce the computational resources required to build it and also toreduce the computational resources required by the driver assistanceprograms.

Starting with the segment upon which the vehicle is currently located,each segment of each path leading away from the current vehicle positionis evaluated for possible inclusion in the electronic horizon. Theelectronic horizon includes a path when the path has at least a minimumthreshold cost or threshold distance.

In one example, the electronic horizon is represented by a tree fromwhich the potential driving paths from the vehicle's current locationdiverge as branches. The tree that forms the electronic horizon includescomponents by which each point along each path can be specified anddefined within the context of the entire tree structure. The tree mayinclude a root node at the current position of the vehicle, a firstsegment for the road link of the root node, multiple internal nodes towhich at least two links of the electronic horizon are attached, one ormore exit links on which the vehicle may potential exit the currentelectronic horizon, and zero or more leaf nodes in which no furthernodes are attached.

The electronic horizon may include positions of potential falsepositives. The potential false positives may be associated with a linkor a node of the electronic horizon. A set of coordinates may describethe locations of the potential false positives. The set of coordinatesmay be measured with respect to the electronic horizon or from alocation of a particular node in the electronic horizon. Alternatively,the set of coordinates may be global coordinates (e.g., longitude andlatitude).

The server 125 or the mobile device 122 may select an electronic horizonincluding false positive positions based on a current position of thevehicle or a route being followed by the vehicle. The size of theelectronic horizon may be a function of the speed of the vehicle, aspeed limit value associated with the current link or a previous link,or a functional classification of the current link. The server 125 orthe mobile device 122 may project likely progression of the vehicle andselect the electronic horizon accordingly.

FIG. 8 illustrates an example map 190 of sign locations 181. The signlocations 181 in FIG. 8 may include all sign locations for the map 190in the database 123. FIG. 9 illustrates an example scheme fordetermining potential false positives for sign placement in the map ofFIG. 8 in which false positive locations for one link are inferred fromknown sign locations on another link.

In one example, “E Street” is the current link that the vehicle istraveling on. The false positive profile for the current link may bebased on sign positions for adjacent links. The mobile device 122 orserver 125 may identify all of the links that intersect the current linkand identify the sign locations for those links within a particularrange of the current link. In the database 123, signs 182 a and 182 bare associated with E street. However, sign 183 a is associated withFirst Street, sign 183 b is associated with Second Street, and sign 183c is associated with Fifth Street. Therefore, the false positive profilefor E street includes signs 183 a-c. Other false positive profiles maybe calculated in a similar manner for other links. For any twointersection links, the confirmed sign locations for a first link makeup the false positive profile for a second link, and the confirmed signlocations for the second link make up the false positive profile for thefirst link.

FIG. 10 illustrates an exemplary mobile device 122 of the system ofFIG. 1. The mobile device 122 includes a processor 200, a memory 204, aninput device 203, a communication interface 205, position circuitry 207,and a display 211. Additional, different, or fewer components arepossible for the mobile device/personal computer 122. FIG. 11illustrates an example flowchart for determining sign locations. Theacts of FIG. 11 may be performed by the mobile device 122, an advanceddriving assistance system (ADAS), a HAD device or an autonomous vehicle,any of which may be referred to as a computing device. The acts may beapplied in a different order. Acts may be omitted or repeated.Additional acts may be added.

At act S101, the processor 200 or the communication interface 205 may beconfigured to receive optical data collected along a path by acollection device. The collection device may be a camera incorporatedinto mobile device 122. The collection device may be an external cameramounted on a vehicle or a helmet. The camera may collect images at aframe rate of 1 frame per second to 10 frames per second or anotherrate.

At act S103, the processor 200 identifies one or more false positivelocations along the path from among the detected sign locations. Thefalse positive locations may be listed in a false positive profile. Thefalse positive profile may be retrieved locally from memory 204 or fromdatabase 123. The false positive profile may be retrieved based on alocation of the mobile device 122 determined by the position circuitry207. In one example, location data from the position circuitry 207described a geographic position that is sent to database 123. A roadsegment is determined from the geographic position, and the falsepositive profile for the road segment is returned to the mobile device122. Alternatively, map data such as locations of the one or more falsepositive locations along the road segment may be sent to the mobiledevice 122. The size or area covered by the map data may be variable.The size may be based on the speed of the vehicle or a function of acharacteristic of the collection device. Example characteristics includerange, focal length, and resolution.

At act S105, the processor 200 analyzes the optical data as a functionof the one or more false positive locations. In one example, portions ofthe optical data within a predetermined distance from the one or morefalse positive locations are omitted from the analysis. In anotherexample, one image processing algorithm is applied to optical data morethan a predetermined distance from the one or more false positivelocations, and another image processing algorithm is applied to theoptical data that is within the predetermined distance from one or morefalse positive locations.

At act S107, the processor 200 identifies at least one confirmed signposition in the optical data. The confirmed sign positions may be signpositions that are not eliminated as false positive positions. Furtherimage processing may interpret data from the signs at the confirmed signpositions. The data may be speed limit values, turn restrictions, curvewarnings, ice warnings, or any data that may cause a driver to changethe operation of a vehicle.

At act S109, the processor 200 generates a command for a drivingfunction based on the at least one confirmed sign position. The drivingfunction may be a speed warning or other informative message for thedriver (e.g., curve approaching, bridge may be icy). The drivingfunction may instruct the engine control unit of the vehicle to changethe operation of the vehicle (e.g., increase accelerator, decreaseaccelerator, apply brake, turn, or other functions).

FIG. 12 illustrates an example network device (e.g., server 125) of thesystem of FIG. 1. The server 125 includes a processor 300, acommunication interface 305, and a memory 301. The server 125 may becoupled to a database 123 and a workstation 128. The workstation 128 maybe used as an input device for the server 125. In addition, thecommunication interface 305 is an input device for the server 125. Incertain embodiments, the communication interface 305 may receive dataindicative of user inputs made via the workstation 128 or the mobiledevice 122. FIG. 13 illustrates an example flowchart for identifyingfalse positives in image data. The acts of the flowchart of FIG. 12 mayalternatively be performed by the server 125 or another computingdevice. Different, fewer, or additional acts may be included.

At act S201, the processor 300 or communication interface 305 receivesoptical data collected along a path by a collection device. The opticaldata may be an image made of pixels. Subsets of pixels may be comparedto sign templates through a window that is moved across the image data.The sign templates may describe expected sign shapes, colors, or otherfeatures in the images. Through, template matching, the processor 200may identify one or more sign locations in the optical data. The signlocations are either actual sign locations of signs intended for thepath segment that the image was collected from or false positive signlocations intended for other path segments.

At act S203, the processor 300 identified one or more false positivelocations along the path. The false positives may be identified by imageprocessing techniques. False positives may be signs at the wrong anglewith respect to the road segment from which the image was collected ortoo far from the road segment. False positives may be identified by ahuman operator who enters data via the workstation 128.

At act S205, the processor 300 generates a false positive profile basedon the false positive positions. The false positive profile maycorrespond to a road segment identifier received from the mobile device122. The false positive profile may list locations along the roadsegment, a route including the road segment, or a larger area includingthe road segment. At act S207, the communication interface 305 sends thefalse positive profile to the mobile device 122.

Supporting Examples

The road link data records may be associated with attributes of or aboutthe roads such as, for example, geographic coordinates, street names,address ranges, speed limits, turn restrictions at intersections, and/orother navigation related attributes (e.g., one or more of the roadsegments is part of a highway or tollway, the location of stop signsand/or stoplights along the road segments), as well as points ofinterest (PO's), such as gasoline stations, hotels, restaurants,museums, stadiums, offices, automobile dealerships, auto repair shops,buildings, stores, parks, etc. The node data records may be associatedwith attributes (e.g., about the intersections) such as, for example,geographic coordinates, street names, address ranges, speed limits, turnrestrictions at intersections, and other navigation related attributes,as well as POls such as, for example, gasoline stations, hotels,restaurants, museums, stadiums, offices, automobile dealerships, autorepair shops, buildings, stores, parks, etc. The geographic data mayadditionally or alternatively include other data records such as, forexample, POI data records, topographical data records, cartographic datarecords, routing data, and maneuver data.

The databases 123 may be maintained by one or more map developers (e.g.,the first company and/or the second company). A map developer collectsgeographic data to generate and enhance the database. There aredifferent ways used by the map developer to collect data. These waysinclude obtaining data from other sources such as municipalities orrespective geographic authorities. In addition, the map developer mayemploy field personnel (e.g., the employees at the first company and/orthe second company) to travel by vehicle along roads throughout thegeographic region to observe features and/or record information aboutthe features. Also, remote sensing such as, for example, aerial orsatellite photography may be used.

The database 123 may be master geographic databases stored in a formatthat facilitates updating, maintenance, and development. For example, amaster geographic database or data in the master geographic database isin an Oracle spatial format or other spatial format, such as fordevelopment or production purposes. The Oracle spatial format ordevelopment/production database may be compiled into a delivery formatsuch as a geographic data file (GDF) format. The data in the productionand/or delivery formats may be compiled or further compiled to formgeographic database products or databases that may be used in end usernavigation devices or systems.

For example, geographic data is compiled (such as into a physicalstorage format (PSF) format) to organize and/or configure the data forperforming navigation-related functions and/or services, such as routecalculation, route guidance, map display, speed calculation, distanceand travel time functions, and other functions, by a navigation device.The navigation-related functions may correspond to vehicle navigation,pedestrian navigation, or other types of navigation. The compilation toproduce the end user databases may be performed by a party or entityseparate from the map developer. For example, a customer of the mapdeveloper, such as a navigation device developer or other end userdevice developer, may perform compilation on a received geographicdatabase in a delivery format to produce one or more compiled navigationdatabases.

The workstation 128 may be a general purpose computer includingprogramming specialized for providing input to the server 125. Forexample, the workstation 128 may provide settings for the server 125.The settings may include verification of sign locations versus falsepositive locations. The settings may include a size for a minimumbounding box or predetermined distance for excluding the false positivelocations from future analysis. The workstation 128 may include at leasta memory, a processor, and a communication interface.

The size of the bounding box or predetermined distance for excludingfalse positive locations may be selected based on a speed of the vehicleor the functional classification of the current road link or future roadlinks. Table 2 lists example classification systems that may be assignednumeric values for functional class. The functional class of the roadsegment may be described as a numerical value (e.g., 1, 2, 3, 4, and 5)represented in the feature vector. Functional class 1 may be highwayswhile functional class 5 may be small streets. Table 2 furtherillustrates schemes having three to six functional classes.

TABLE 2 Simple Complex U.S. Long Highway System System Distance RoadsTags Arterial Road Interstates Interstate Expressway Motorway CollectorPrincipal Arteries Federal Highway Trunk Road Local Road Minor ArteriesState Highway Primary Major Collector County Highway Secondary MinorCollector Local Road Tertiary Local Road Residential

One example of a simple system includes the functional classificationmaintained by the United States Federal Highway administration. Thesimple system includes arterial roads, collector roads, and local roads.The functional classifications of roads balance between accessibilityand speed. An arterial road has low accessibility but is the fastestmode of travel between two points. Arterial roads are typically used forlong distance travel. Collector roads connect arterial roads to localroads. Collector roads are more accessible and slower than arterialroads. Local roads are accessible to individual homes and business.Local roads are the most accessible and slowest type of road.

An example of a complex functional classification system is the urbanclassification system. Interstates include high speed and controlledaccess roads that span long distances. The arterial roads are dividedinto principle arteries and minor arteries according to size. Thecollector roads are divided into major collectors and minor collectorsaccording to size. Another example functional classification systemdivides long distance roads by type of road or the entity in control ofthe highway. The functional classification system includes interstateexpressways, federal highways, state highways, local highways, and localaccess roads. Another functional classification system uses the highwaytag system in the Open Street Map (OSM) system. The functionalclassification includes motorways, trunk roads, primary roads, secondaryroads, tertiary roads, and residential roads.

The computing device processor 200 and/or the server processor 300 mayinclude a general processor, digital signal processor, an applicationspecific integrated circuit (ASIC), field programmable gate array(FPGA), analog circuit, digital circuit, combinations thereof, or othernow known or later developed processor. The mobile device processor 200and/or the server processor 300 may be a single device or combinationsof devices, such as associated with a network, distributed processing,or cloud computing. The computing device processor 200 and/or the serverprocessor 300 may also be configured to cause an apparatus to at leastperform at least one of methods described above.

The memory 204 and/or memory 301 may be a volatile memory or anon-volatile memory. The memory 204 and/or memory 301 may include one ormore of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 204 and/or memory 301 may be removablefrom the mobile device 122, such as a secure digital (SD) memory card.

The communication interface 205 and/or communication interface 305 mayinclude any operable connection. An operable connection may be one inwhich signals, physical communications, and/or logical communicationsmay be sent and/or received. An operable connection may include aphysical interface, an electrical interface, and/or a data interface.The communication interface 205 and/or communication interface 305provides for wireless and/or wired communications in any now known orlater developed format.

In the above described embodiments, the network 127 may include wirednetworks, wireless networks, or combinations thereof. The wirelessnetwork may be a cellular telephone network, an 802.11, 802.16, 802.20,or WiMax network. Further, the network 127 may be a public network, suchas the Internet, a private network, such as an intranet, or combinationsthereof, and may utilize a variety of networking protocols now availableor later developed including, but not limited to TCP/IP based networkingprotocols.

While the non-transitory computer-readable medium is described to be asingle medium, the term “computer-readable medium” includes a singlemedium or multiple media, such as a centralized or distributed database,and/or associated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” shall also include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in this application, the term “circuitry” or “circuit” refers toall of the following: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of “circuitry” applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., E PROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

I claim:
 1. A method comprising: receiving sensor data opticallycollected along a path by a collection device; identifying one or morefalse positive locations for one or more signs associated with the path;identifying a potential position from the sensor data; comparing thepotential position to the one or more false positive locations; andanalyzing the sensor data based on the one or more false positivelocations to identify the potential position as a confirmed position inresponse to a distance between the potential position and at least oneof the one or more false positive locations exceeding a thresholddistance.
 2. The method of claim 1, further comprising: modifying theidentified potential position in response to the distance between thepotential position and the at least one of the one or more falsepositive locations being less than the threshold distance.
 3. The methodof claim 1, wherein analyzing the sensor data based on the one or morefalse positive locations comprises: defining a bounding box for thesensor data according to the one or more false positive locations. 4.The method of claim 3, further comprising: blocking analysis of thesensor data in the bounding box.
 5. The method of claim 3, furthercomprising: generating a warning message indicative of a risk of falsepositives based on the bounding box.
 6. The method of claim 3, furthercomprising: increasing the threshold distance for the bounding box. 7.The method of claim 1, further comprising: receiving a set of map dataincluding the one or more false positive locations along the path.
 8. Amethod comprising: receiving sensor data optically collected along apath by an autonomous vehicle; identifying one or more false positivelocations for one or more signs associated with the path; identifying apotential position from the sensor data; comparing the potentialposition to the one or more false positive locations; analyzing thesensor data based on the one or more false positive locations to confirmthe potential position as a confirmed position; and generating a commandfor the autonomous vehicle in response to the confirmed position.
 9. Themethod of claim 8, wherein the command includes data indicative of aroad sign for the path.
 10. The method of claim 8, wherein the commandis a speed warning.
 11. The method of claim 8, wherein the command is acommand for operation of the autonomous vehicle.
 12. The method of claim8, wherein analyzing the sensor data based on the one or more falsepositive locations comprises: defining a bounding box for the sensordata according to the one or more false positive locations.
 13. Themethod of claim 12, further comprising: blocking analysis of the sensordata in the bounding box.
 14. The method of claim 12, furthercomprising: generating a warning message indicative of a risk of falsepositives based on the bounding box.
 15. The method of claim 12, furthercomprising: increasing the size for the bounding box.
 16. A methodcomprising: identifying a path; identifying a potential position fromsensor data optically collected along the path by a collection device;performing a comparison of the potential position to a threshold valuewith respect to the path; identifying the potential position as a falsepositive location for a sign based on the comparison; and storing dataindicative of the false positive location in a geographic database. 17.The method of claim 16, wherein the threshold value is a thresholddistance between the potential position and the path.
 18. The method ofclaim 16, wherein the threshold value is an orientation angle thresholdfor the potential position.
 19. The method of claim 16, furthercomprising: defining a bounding box for the sensor data according to thefalse positive location; and storing the bounding box in the geographicdatabase.
 20. The method of claim 16, wherein the threshold valueincludes an angle, position, or orientation of a sign detected in theoptical data.